... small and energy- limited batteries Examples of such networks include mobile cellular systems, wireless local area networks, wireless ad hoc networks, and wireless sensor networks In these energy- constrained. .. SUMMARY Recently, cross- layer design has been identified as a promising approach which achieves good performance for energy- constrained wireless networks In general, cross- layer design refers... energyefficient scheduling and transmission strategies for wireless networks In doing so, we adopt the cross- layer design approach, which designs and controls the operations of different layers of the network
CROSS-LAYER SCHEDULING AND TRANSMISSION STRATEGIES FOR ENERGY-CONSTRAINED WIRELESS NETWORKS HOANG ANH TUAN (B.Eng Hons., Uni of Sydney) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2005 ACKNOWLEDGEMENTS First and foremost, I would like to express my sincerest gratitude to my supervisor, Dr Mehul Motani, for so much time and effort he has spent on guiding me through every state of this thesis Dr Motani has been a continuous source of ideas, encouragement, motivation, and support for me While always being available to help, he also gave me room to independently explore different directions; this clearly made the research work much more enjoyable for me I feel truly fortunate to have been working under Dr Motani’s guidance Next, I would like to thank friends in my research group, including Vineet Srivastava, Lawrence Ong, Kok Kiong Yap, and Hon Fah Chong, for many interesting research discussions that not only improved my thesis, but also broadened my knowledge In particular, I want to give a special thanks to Vineet for his true friendship I want to thank my parents, brother Dung, and sister Phuong, for their love and care Especially, I am indebted to my parents for so many sacrifices they have made for me My parents have made me who I am Finally, I am most grateful to my wife, Cuc, and my ‘little Pinocchio’, Minh, for sharing this journey with me It was tough to have a hubby who was usually lost in models and equations; it was also tough to have a daddy who was usually busy at weekends Despite all this, Cuc and Minh have always been the ones I turned to for love and encouragement I thank them for being a part of my life iii TABLE OF CONTENTS Acknowledgements iii Table of Contents iv Summary ix List of Tables xi List of Figures xii List of Abbreviations xviii Introduction 1.1 1.2 1.3 Energy-constrained Wireless Networks 1.1.1 Infrastructure-based Wireless Networks 1.1.2 Infrastructure-less Wireless Networks Design Approaches 1.2.1 Layered Architectures and Layered Design 1.2.2 Cross-layer Design Thesis Focus and Contributions 10 1.3.1 Problem 1: Cross-layer Adaptive Transmission for Singleuser Systems 1.3.2 Problem 2: Cross-layer Adaptive Scheduling / Transmission in Multiple-access Systems 1.3.3 1.4 2.2 2.3 13 Problem 3: Combining Scheduling, Broadcasting, and Data Compression in Sensor Networks 15 Organization of Thesis 17 Cross-layer Scheduling and Transmission Strategies 2.1 11 20 General System Model 20 2.1.1 Data Arrival Processes and Buffer Dynamics 22 2.1.2 Finite-state Markov Channels 22 Capacity-achieving Strategies for Fading Channels 25 2.2.1 Single-user Scenario 25 2.2.2 Multiple-access Scenario 27 Taking Arrival Statistics and Buffer Occupancies into Account 29 2.3.1 System Throughput 30 2.3.2 Buffer and Channel Adaptive Policies 31 iv 2.4 A Cross-layer Strategy under Deterministic Data Arrival and Deterministic Channel 2.5 32 2.4.1 A Periodic Sensing Scenario with Spatial Data Correlation 33 2.4.2 Compression of Correlated Information Sources 2.4.3 Exploiting Wireless Broadcast Property for Data Com- 33 pression 35 Summary 36 Buffer and Channel Adaptive Transmission: Fully Observable System States 38 3.1 Related Work 40 3.2 Problem Definition 43 3.2.1 System Model 43 3.2.2 Adaptive Transmission 44 3.2.3 Transmission Errors 46 3.2.4 Throughput Maximization Problem 47 Satisfying a BER Constraint 48 3.3.1 Optimal Policies (with a BER Constraint) 50 3.3.2 Structure of Optimal Policies 53 Removing the BER Constraint 56 3.4.1 Taking Transmission Errors into Account 57 3.4.2 Optimal Policies (without the BER Constraint) 58 Numerical Results and Discussion 59 3.5.1 System Parameters 59 3.5.2 An Interesting Structural Property 61 3.5.3 Packet Loss due to Buffer Overflow 62 3.5.4 Packet Loss due to Buffer Overflow and Transmission Errors 66 3.3 3.4 3.5 3.6 Conclusion 69 Buffer and Channel Adaptive Transmission: Incomplete System State Information 71 4.1 Incomplete System State Information 73 4.1.1 Quantized Buffer State Information 74 4.1.2 Delayed Error-free Channel State Information 75 4.1.3 Non-delayed Imperfect Channel Estimates 75 4.1.4 Delayed Imperfect Channel Estimates 77 v 4.2 4.3 4.4 Adaptive Transmission under Incomplete SSI - General Approaches 77 4.2.1 Employing the MDP Policy π ∗ 78 4.2.2 Partially Observable MDPs 79 Optimal Policies Given Delayed Error-free Channel States 80 4.3.1 Case When m = 0, n = 81 4.3.2 Case When n = 82 Policies Given Imperfect Channel Estimates 83 4.4.1 Optimal Policies Given Delayed Imperfect Channel Estimates with i.i.d Channel Model 4.4.2 4.5 Heuristic Policies Given Delayed Imperfect Channel Estimates 84 Numerical Results and Discussion 87 4.5.1 System Parameters 87 4.5.2 Performance of MDP Policies Given Quantized Buffer Occupancy and Perfect Channel State 4.5.3 4.5.4 89 Performance of Different Approaches Given Delayed Errorfree Channel State 4.6 83 91 Performance of Different Approaches Given Imperfect Channel Estimates 94 Conclusion 96 Buffer and Channel Adaptive Scheduling/Transmission for Multipleaccess Wireless Channels 98 5.1 Related Work 101 5.2 Problem Description 104 5.2.1 System Model and General Notation 104 5.2.2 Cross-layer Adaptive Scheduling/Transmission Policies 5.2.3 5.3 106 Throughput Maximization Problem 108 Solving the Throughput Maximization Problem 109 5.3.1 Converting into a Non-constrained Optimization Problem 109 5.3.2 Markov Decision Process 112 5.3.3 Complexity of Obtaining and Implementing Throughput Maximizing Policies 113 5.4 Statistics-oblivious Adaptive Scheduling Policies 114 vi 5.5 Max-gain Scheduling Optimal Transmission 115 5.5.1 Max-gain Scheduling Adaptive Transmission Policies 116 5.5.2 Obtaining Max-gain Scheduling Optimal Transmission Policies 117 5.5.3 Complexity of Obtaining and Implementing Max-gain Scheduling Optimal Transmission Policies 118 5.6 Round-robin Scheduling Optimal Transmission 119 5.6.1 Round-robin Scheduling Optimal Transmission Policies 5.6.2 Obtaining Round-robin Scheduling Optimal Transmission Policies 5.6.3 119 120 Complexity of Obtaining and Implementing Round-robin Scheduling Optimal Transmission Policies 122 5.7 Numerical Results and Discussion 123 5.7.1 System Parameters 123 5.7.2 Performance of Different Adaptive Scheduling/ Transmission Schemes 124 5.8 Hybrid Scheduling Schemes 129 5.8.1 Combined Round-robin and Max-gain Scheduling 129 5.8.2 Combined Round-robin and Optimal Scheduling 130 5.8.3 Hybrid Scheduling Optimal Transmission Policies 130 5.8.4 Performance of Hybrid Scheduling Optimal Transmission Policies 5.9 131 Observations and Conclusions 133 Joint Scheduling, Transmission, and Source Compression in Sensor Networks 135 6.1 Motivations 136 6.2 Related Work 140 6.3 Model of A Cluster-based Wireless Sensor Network 142 6.3.1 Network Architecture 142 6.3.2 Sensing and Communication 144 6.3.3 Energy Model for Wireless Sensor Nodes 145 6.3.4 Direct Transmission versus Multihopping 146 6.3.5 Spatial Correlation and Data Compression 148 vii 6.4 6.5 6.6 6.7 6.8 6.9 Collaborative Broadcasting and Compression: A Simple Case 149 6.4.1 A Simple Cluster-based Sensor Network 149 6.4.2 Incentives for Collaboration 150 6.4.3 Maximizing the Lifetime of the Node Who Dies First 151 Collaborative Broadcasting and Compression: A general network 154 6.5.1 General Notation 154 6.5.2 Control During Each Data-gathering Round 155 6.5.3 Control over Multiple Data-gathering Rounds 156 6.5.4 Sensor Lifetime and System Performance 157 Lifetime Vector Optimization Problem 158 6.6.1 A General Approach to Solve the LVO Problem 159 6.6.2 Linear Programming Formulation 160 Heuristic Algorithm 163 6.7.1 A CBC Policy for T Data-gathering Rounds 163 6.7.2 A Heuristic CBC Scheme for Phase 165 6.7.3 Complexity of Heuristic Algorithm 165 Reflections on the CBC Approach 168 6.8.1 Startup Cost of Sensor Nodes 168 6.8.2 Packet Transmission Errors 169 6.8.3 Effects on the Relaying Network 170 Numerical Study 171 6.9.1 Experimental Model 172 6.9.2 Results and Discussion 173 6.10 Conclusion 178 Conclusions and Future Work 180 A Proof of Lemma 3.3.1 184 B Proof of Lemma 5.3.2 189 C Publication List 191 C.1 International Conferences 191 C.2 Book Chapter 192 C.3 Journals 192 Bibliography 194 viii SUMMARY Recently, cross-layer design has been identified as a promising approach which achieves good performance for energy-constrained wireless networks In general, cross-layer design refers to the methodology in which multiple layers in the communication protocol stack are designed in an integrated manner, with the intra-layer and inter-layer dynamics being taken into account In this thesis, we study cross-layer scheduling and transmission strategies that provide good system performance, in terms of throughput, while conserving nodes’ energy First, we consider a cross-layer adaptive transmission problem for singleuser systems with stochastic data arrivals, finite-length buffer operating over a time-varying wireless channel The objective is to adapt the transmit power and rate according to the buffer and channel conditions so that the system throughput is maximized, subject to an average transmit power constraint We demonstrate that this problem can be solved by reformulating it as a Markov decision process We then identify an important structural characteristic of the throughput optimal policy, which is in sharp contrast to the structure of policies that achieve capacity of fading channels We also consider the adaptive transmission problem when only a partial observation of the buffer or channel states is available Next, we consider a multiple-access scenario in which multiple users share a single channel to transmit data to a center node There are two control decisions to be made in each time slot, i.e., a scheduling decision which assigns the channel to one of the users, and a transmission decision which sets the transmit power and rate All scheduling/transmission policies employed must satisfy the average transmit power constraint of each node We first look at ix the problem of finding the optimal cross-layer adaptive scheduling/transmission policy which adapts to the buffer and channel conditions of all users so that the total system throughput is maximized We then use the performance of this optimal policy as a benchmark to assess the performance of simpler adaptive scheduling/transmission schemes which also adapt to the buffer and channel conditions This allows us to draw some useful guidelines for controlling energyconstrained multiple-access systems Finally, we study a problem of combining scheduling, transmission, and data compression to conserve energy in a spatially correlated cluster-based sensor networks Since wireless transmission is inherently broadcast, when one sensor node transmits data to the cluster head, other nodes in its coverage area can receive the transmitted data When data collected by different sensors are correlated, each sensor can utilize the data it overhears from others’ transmissions to compress its own data and conserve energy in its own transmissions Based on this observation, we formulate a problem in which sensors in each cluster are scheduled to transmit so that they can collaborate in joint source compression in order to maximize the network lifetime We show that this lifetime optimization problem can be solved by a sequence of linear programming problems We also develop a heuristic scheme which has low complexity and achieves near optimal performance x 187 Proof When the channel is in state 0, no transmission is possible, therefore K−1 ∞ Jα∗ (b1 , 0) = CI (b1 , 0, 0) + α PG (0, g)pA(a)Jα∗ q(b1 , a), g , g=0 a=0 (A.9) K−1 ∞ Jα∗ (b2 , 0) = CI (b2 , 0, 0) + α PG (0, g)pA(a)Jα∗ q(b2 , a), g g=0 a=0 Therefore, Jα∗ (b2 , 0) − Jα∗ (b1 , 0) = CI (b2 , 0, 0) − CI (b1 , 0, 0) K−1 ∞ +α g=0 a=0 PG (0, g)pA (a) Jα∗ q(b2 , a), g − Jα∗ q(b1 , a), g (A.10) > CI (b2 , 0, 0) − CI (b1 , 0, 0) = β × L(b2 , 0) − L(b1 , 0) The inequality in (A.10) is due to Lemma A.0.1 From (A.10), it is clear that Jα∗ (b2 , 0) − Jα∗ (b1 , 0) increases without bound when β increases and the proof is completed Using the results of Lemmas A.0.1, A.0.2, and A.0.3, let us prove the Lemma 3.3.1 Lemma 3.3.1 For each buffer state b > 1, there exists a constant βo such that for every β > βo and ≤ u1 < u2 ≤ b, the following inequality holds: ∆I (b, 1, u1 , u2) − ∆I (b, 2, u1 , u2) < ∆F (b, 1, u1 , u2 ) − ∆F (b, 2, u1 , u2) (A.11) Proof First of all, we have ∆I (b, 1, u1 , u2) − ∆I (b, 2, u1 , u2) = W No f (u2, Pb ) − f (u1, Pb ) 1 − γ1 γ2 (A.12) 188 Therefore, the left hand side of (A.11) does not depend on β For the right hand side of (A.11), we have: K−1 ∞ ∆F (b, g, u1, u2 ) =α g ′ =0 a=0 PG (g, g ′)pA (a)(Jα∗ (q(b − u1 , a), g ′) (A.13) − Jα∗ (q(b − u2 , a), g ′)) Now ∆F (b, 1, u1, u2 ) − ∆F (b, 2, u1, u2 ) K−1 ∞ =α g ′ =0 a=0 PG (1, g ′) − PG (2, g ′) × pA (a) (A.14) × Jα∗ q(b − u1 , a), g ′ − Jα∗ q(b − u2 , a), g ′ K−1 ∞ =α g ′ =1 a=0 PG (1, g ′) − PG (2, g ′) × pA (a) × Jα∗ q(b − u1 , a), g ′ − Jα∗ q(b − u2, a), g ′ (A.15) ∞ +α a=0 PG (1, 0) − PG (2, 0) × pA (a) × Jα∗ q(b − u1 , a), − Jα∗ q(b − u2 , a), When β increases, from Lemmas A.0.1 and A.0.2, the first term in (A.15) is always lower bounded while from Lemma A.0.3, the second term increases without bound This combined with (A.12) completes the proof APPENDIX B PROOF OF LEMMA 5.3.2 Lemma 5.3.2 For any stationary feasible adaptive scheduling/transmission policy φ ∈ Ψst , let Lφo be the total packet loss rate of all users and Pnφ be the average power consumed by user n when φ is employed, there exists a nonstationary policy ψ ∈ Ψ such that Lψo = Lφo while Pmψ = N N Pnφ , n=1 ∀m ∈ N , (B.1) where Lψo is the total packet loss rate and Pmψ is the average power consumed by user m when policy ψ is employed Proof Given a stationary policy φ ∈ Ψst , we will construct a non-stationary policy ψ ∈ Φ that satisfies (5.7) This is done by first formulating N − other stationary policies, φ1 , φ2 , φN −1 , and then time sharing φ, φ1 , φ2 , φN −1 Note again that each stationary scheduling/transmission policy φk , k = 1, 2, N − 1, is completely specified by the vector of transmission rates assigned to the N users in each system state In time slot i, the system state is S i = k k k (Bi1 , Bi2 , BiN , G1i , G2i , GN i ) Let I , B i , and Gi be the N-element vec- tors obtained after carrying out k right cyclic shifts on vectors (1, 2, N), (Bi1 , Bi2 , BiN ), and (G1i , G2i , GN i ) respectively We then set φk (S i , n) = φ(S ki , I k (n)) (B.2) where S ki = (B ki , Gki ) and I k (n) is the nth element of I k Note that policy φk is nothing but policy φ being applied to a modified system in which the sequence of N users is permutated by carrying out k right cyclic shifts As all N users in the system are symmetric, i.e., they have i.i.d data arrival processes, i.i.d 189 190 channel processes, and the same buffer lengths, when φk is employed the total packet loss rate and average transmit powers are Lφo k = Lφo , k while Pnφ = P k (n) (B.3) where P k is the vector obtained after k right cyclic shifts of (P1φ , P2φ , PNφ ) For convenient of notation, we let φ0 = φ Now, based on N stationary policies φ0 , φ1 , φ2 , φN −1 , we construct a non-stationary adaptive scheduling/transmission policy ψ = {φi} that satisfies φi = φk , where k = mod (i, Nt) , t ∀i = 0, 1, 2, (B.4) Note that in (B.4), mod (x, y) gives the remainder on division of x by y while ⌊x⌋ is the floor function To put it simple, the system is control on a frame-byframe basis, each frame is of length Nt time slots During each frame, N policies φ0 , φ1 , φ2 , φN −1 are employed sequentially, each for t consecutive time slots When t → ∞ the averaging effect takes place and during each frame, we have the average total packet loss rate is Lφo while the average power consumed by user m is N N −1 k Pmφ k=0 This shows that ψ satisfies (B.1) = N N Pnφ n=1 APPENDIX C PUBLICATION LIST C.1 International Conferences A T Hoang and M Motani Exploiting Wireless Broadcast in SpatiallyCorrelated Sensor Networks In Proceedings 2005 IEEE International Conference on Communications (ICC), May 2005 A T Hoang and M Motani Collaborative broadcasting and compression in cluster-based wireless sensor networks In Proceedings 2nd European Workshop on Wireless Sensor Networks (EWSN), Jan 2005 A T Hoang and M Motani Decoupling multiuser cross-layer adaptive transmission In Proceedings 2004 IEEE International Conference on Communications (ICC), June 2004, pp 3061-3065 A T Hoang and M Motani Buffer and channel adaptive transmission over fading channels with imperfect channel state information In Proceedings 2004 IEEE Wireless Communications and Networking Conference (WCNC), Mar 2004, pp 1891-1896 A T Hoang and M Motani Adaptive sensing and transmitting for increasing lifetime of energy constrained sensor networks In Proceedings 38th Annual Conference on Information Sciences and Systems (CISS), Mar 2004, Princeton University M Motani and A T Hoang An instance of multiuser diversity in wireless networks In Proceedings 2003 IEEE International Symposium on Information Theory (ISIT), July 2003, p 443 191 192 A T Hoang and M Motani Buffer and channel adaptive modulation for transmission over fading channels In Proceedings 2003 IEEE International Conference on Communications (ICC), July 2003, pp 2748-2752 A T Hoang and M Motani Buffer control using adaptive MQAM for wireless channels” In Proceedings IFIP TC6/WG6.8 Working Conference on Personal Wireless Communications (PWC), Oct 2002, pp 77-104 C.2 Book Chapter A T Hoang and M Motani Adaptive sensing and reporting in energy constrained sensor networks Sensor Network Operations, Wiley-IEEE Press, May 2006 C.3 Journals A T Hoang and M Motani Cross-layer adaptive transmission: Optimal strategies in fading channels Under review for IEEE Transactions on Communications A T Hoang and M Motani Cross-layer adaptive transmission: Coping with incomplete system state information Submitted to IEEE Transactions on Wireless Communications A T Hoang and M Motani Collaborative broadcasting and compression in cluster-based wireless sensor networks Under review for ACM Transactions on Sensor Networks 193 A T Hoang and M Motani Adaptive Scheduling and Transmission for multiple access wireless channels In preparation BIBLIOGRAPHY [AKR+ 01] M Andrews, K Kumaran, K Ramanan, A Stolyar, R Vijayakumar, and P Whiting Providing quality of service over a shared wireless link IEEE Communications Magazine, 39(2):150–154, Feb 2001 [AKR+ 04] M Andrews, K Kumaran, K Ramanan, A L Stolyar, R Vijayakumar, and P Whiting Scheduling in a queueing system with asynchronously varying service rates Probability in the Engineering and Informational Sciences, 18:191–217, 2004 [AN98] A T Andersen and B F Nielsen A markovian approach for modeling packet traffic with long-range dependence IEEE Journal on Selected Areas in Communications, 16(5):719–732, 1998 [ANJ05] S Agnihotri, P Nuggehalli, and H S Jamadagni On maximizing lifetime of a sensor network In Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Taormina/Giardini Naxos, June 2005 [ASSC02] I.F Akyildiz, W Su, Y Sankarasubramaniam, and E Cayirci A survey on sensor networks IEEE Communications Magazine, 40(8):102–114, August 2002 [BG02] R A Berry and R G Gallager Communication over fading channels with delay constraints IEEE Transactions Information Theory, 48(5):1135–1149, May 2002 [BTD01] S Basagni, D Turgut, and S K Das Mobility-adaptive protocol for managing large ad hoc networks In Proceedings of IEEE International Conference on Communications (ICC), pages 1539–1543, Jun 2001 [BV04] S Boyd and L Vandenberghe Convex Optimization Cambridge University Press, 1st edition, 2004 [BW01] S Borst and P Whiting Dynamic rate control algorithms for HDR throughput optimization In Proceedings of IEEE INFOCOM 2001, pages 976–985, Anchorage, Alaska, Apr 2001 [CC99] B Collins and R Cruz Transmission policy for time varying channel with average delay constraints In Proceedings of 1999 Allerton Conf on Commun Control and Comp, pages 1–9, Monticello, IL, 1999 194 195 [CG05] X Cai and G B Giannakis Adaptive psma accounting for channel estimation and prediction errors IEEE Transactions Wireless Communications, 4(1):246–256, Jan 2005 [CLV04] R Cristescu, B Lozano, and M Vetterli On network correlated data gathering In Proceedings of IEEE INFOCOM 2004, pages 2571–2582, Hong Kong, March 2004 [CPR03] J Chou, D Petrovic, and K Ramchandran A distributed and adaptive signal processing approach to reducing energy consumption in sensor networks In Proceedings of IEEE INFOCOM 2003, pages 1054–1062, San Francisco, March 2003 [CWKS97] B P Crow, I Widjaja, J G Kim, and P T Sakai IEEE 802.11 wireless local area networks IEEE Communications Magazine, 35(9):116–126, Sep 1997 [DG97] T Dean and R Givan Model minimization in Markov decision processes In Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAAI, 1997, pages 106–111, 1997 [DGM+ 04] A Deshpande, C Guestrin, S R Madden, J M Hellerstein, and W Hong Model-driven data acquisition in sensor networks In Proceedings of 30th International Conference on Very Large Data Bases (VLDB), pages 588–599, August 2004 [DMS+ 03] K Das, J Marks, E Sharkawi, P Arabshahi, and A Gray Minimum power broadcast trees for wireless networks: Integer programming formations In Proceeding of IEEE INFOCOM’03, San Francisco, March 2003 [FS83] G J Foschini and J Salz Digital communications over fading radio channels Bell Syst Tech J., pages 429–456, February 1983 [Gal68] R G Gallager Information Theory and Reliable Communication New York: Wiley, 1968 [GC97] A J Goldsmith and S G Chua Variable-rate variable-power mqam for fading channels IEEE Transactions on Communications, 45(10):1218–1230, October 1997 [GE03] A Goel and D Estrin Simultaneous optimization for concave cost: Single sink aggregation or single source buy-at-bulk In Proceedings of ACM-SIAM Symposium on Discrete Algorithms, pages 499–505, 2003 196 [GKS03] M Goyal, A Kumar, and V Sharma Power constrained and delay optimal policies for scheduling transmission over a fading channel In Proceedings of IEEE INFOCOM’03, pages 311–320, San Francisco, March 2003 [Goe99] D L Goeckel Adaptive coding for time-varying channel using outdated fading estimates IEEE Transactions Communications, 47(6):844–855, Jun 1999 [Gud91] M Gudmundon Correlation model for shadow fading in mobile radio systems Electronics Letters, 27(23):2145–2146, 1991 [GV97] A J Goldsmith and P P Varaiya Capacity of fading channels with channel side information IEEE Transactions Information Theory, 43(6):1986–1992, November 1997 [GW02] A J Goldsmith and S B Wicker Design challenges for energyconstrained ad hoc wireless networks IEEE Wireless Communications Magazine, 9(4):8–27, August 2002 [Haa00] J C Haartsen The bluetooth radio system IEEE Personal Communications, 7(1):28–36, 2000 [HCB00] W B Heinzelman, A P Chandrakasan, and H Balakrishnan Energy-efficient communication protocol for wireless sensor networks In Proceedings of the 33th Hawaii International Conference on System Sciences (HICSS-33), pages 1–10 IEEE Computer Society, January 2000 [HCB02] W B Heinzelman, A P Chandrakasan, and H Balakrishnan An application-specific protocol architecture for wireless microsensor networks IEEE Transactions Wireless Communications, 1(4):660– 670, October 2002 [HGG02] T Holliday, A J Goldsmith, and P Glynn Wireless link adaptation policies: Qos for deadline constrained traffic with imperfect channel estimates In Proceedings of IEEE International Conference on Communications (ICC), New York, Apr 2002 [HL95] F Hiller and G Lieberman Introduction to Operation Research McGraw-Hill Inc, 1995 [HM02] A T Hoang and M Motani Buffer control using adaptive mqam for wireless channels In Proceedings of IFIP TC6/WG6.8 Working Conference on Personal Wireless Communications (PWC 2002), pages 97–104, Singapore, October 2002 197 [HM03] A T Hoang and M Motani Buffer and channel adaptive modulation for transmission over fading channels In Proceedings of IEEE International Conference on Communications (ICC 2003), pages 2748–2752, Anchorage, Alaska, July 2003 [HM04a] A T Hoang and M Motani Buffer and channel adaptive transmission over fading channels with imperfect channel state information In Proceedings of IEEE Wireless Communications and Networking Conference (WCNC 2004), pages 1891–1896, Atlanta, Mar 2004 [HM04b] A T Hoang and M Motani Decoupling multiuser cross-layer adaptive transmission In Proceedings of IEEE International Conference on Communications (ICC 2004), pages 3061–3065, Paris, Jun 2004 [HM05a] A T Hoang and M Motani Collaborative broadcasting and compression in cluster-based wireless sensor networks In Proceedings 2nd European Workshop on Wireless Sensor Networks (EWSN 2005), pages 197–206, Istanbul, January 2005 [HM05b] A T Hoang and M Motani Collaborative broadcasting and compression in cluster-based wireless sensor networks Under review for ACM Transactions on Sensor Networks, 2005 [HM05c] A T Hoang and M Motani Cross-layer adaptive transmission: Coping with incomplete system state information Submitted to IEEE Transactions on Wireless Communications, 2005 [HM05d] A T Hoang and M Motani Cross-layer adaptive transmission: Optimal strategies in fading channels Under review for IEEE Transactions on Communications, 2005 [HM05e] A T Hoang and M Motani Exploiting wireless broadcast in spatially correlated sensor networks In Proceedings of IEEE International Conference on Communications (ICC 2005), Seoul, Korea, May 2005 [IGE00] C Intanagonwiwat, R Govindan, and D Estrin Directed diffusion: A scalable and robust communication paradigm for sensor networks In Proceedings of ACM MobiCom’00, Boston, Aug 2000 [Jak74] W C Jakes Microwave Mobile Communications John Willey and Sons, 1974 [JP04] A Jindal and K Psounis Modeling spatially-correlated sensor network data In Proceedings of IEEE International Conference on Sensor and Ad hoc Communications and Networks (SECON) 2004, pages 162–171, 2004 198 [KDN03] K Kalpakis, K Dasgupta, and P Namjoshi Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks ACM Computer Networks, 42(6):697–716, Aug 2003 [KEW02] B Krishnamachari, D Estrin, and S Wicker Modeling data centric routing in wireless sensor networks In USC Computer Engineering Technical Report CENG, pages 12–44, 2002 [KH95] R Knopp and P Humblet Information capacity and power control in single cell multiuser communications In Proceedings of IEEE International Conference on Communications (ICC’95), pages 331– 335, Seattle, June 1995 [KK05] V Kawadia and P R Kumar A cautionary perspective on cross layer design IEEE Wireless Communication Magazine, 12(1):3–11, 2005 [KKP99] J M Kahn, R H Katz, and K S J Pister Next century challenges: Mobile networking for smart dust In Proceedings ACM MobiCom 99, pages 271–278, 1999 [KQ03] K Kumaran and L Qian Uplink scheduling in CDMA packet-data systems In Proceedings of IEEE INFOCOM 2003, San Francisco, Mar 2003 [KV86] P R Kumar and P Varaiya Stochastic Systems: estimation, identification, and adaptive control Englewood Cliffs, N.J.: Prentice Hall, 1986 [LBH03] P Liu, R Berry, and M L Honig Delay-sensitive packet scheduling in wireless networks In Proceedings of IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, Mar 2003 [LCK95] M L Littman, A R Cassandra, and L P Kaelbling Learning policies for partially observable environments: Scaling up In Proceedings of Twelfth International Conference on Machine Learning, pages 362–370, 1995 [LCS01] X Liu, E K P Chong, and N B Shroff Opportunistic transmission scheduling with resource-sharing constraints in wireless networks IEEE Journal of Selected Areas in Communications, 19(10):2053–2065, Oct 2001 [LK03] Y Liu and E Knightly Opportunistic fair scheduling over multiple wireless channels In Proceedings of IEEE INFOCOM 2003, San Francisco, Mar 2003 199 [Lov91] W S Lovejoy A survey of algorithmic methods for partially observable markov decision processes Annals of Operation Research, 28(1):47–65, 1991 [ML02] D Melo and M Liu Analysis of energy consumption and lifetime of heterogeneous wireless sensor networks In Proceeding of IEEE Globecom 2002, 2002 [NMR03] M J Neely, E Modiano, and C E Rohrs Power allocation and routing in multibeam satellites with time-varying channels IEEE/ACM Transactions on Networking, 11(1):138–152, 2003 [NPB95] I Nourbakhsh, R Powers, and S Birchfield Dervish an officenavigating robot AI Magazine, 16(2):53–60, 1995 [OHH04] G E Oien, H Holm, and K J Hole Impact of channel prediction on adaptive coded modulation performance in rayleigh fading IEEE Transactions on Vehicular Technology, 53(3):758–769, May 2004 [PKG04] S Pattem, B Krishnamachari, and R Govindan The impact of spatial correlation on routing with compression in wireless sensor networks In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks, pages 28–35, New York, April 2004 ACM Press [PR99] S Pradhan and K Ramchandran Distributed source coding using syndromes: Design and construction In Proceedings of the Data Compression Conference (DCC), page 158, Washington, DC, USA, 1999 IEEE Computer Society [Pro01] J G Proakis Digital Communications McGraw-Hill International, 4th edition, 2001 [QB04] X Qin and R Berry Opportunistic splitting algorithms for wireless networks In Proceedings of IEEE INFOCOM 2004, Hong Kong, Mar 2004 [Rap96] T Rappaport Ad hoc Mobile Wireless Networks: Protocols and Systems Prentice Hall, 1996 [RSA04] D Rajan, A Sabharwal, and B Aazhang Delay bounded packet scheduling of bursty traffic over wireless channels IEEE Transactions on Information Theory, 50(1):125–144, 2004 [RSPS02] V Raghunathan, C Schurgers, S Park, and M Srivastava Energy aware wireless microsensor networks IEEE Signal Processing Magazine, 19(2):40–50, March 2002 200 [SCI+ 01] E Shih, S Cho, N Ickes, R Min, A Sinha, A Wang, and A Chandrakasan Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks In Proceedings of the 7th ACM International Conference on Mobile Computing and Networking (MobiCom 2001), pages 272–287 ACM Press, July 2001 [SRB01] A Sabharwal, D Rajan, and B.Aazhang Dual problems in power control In Proceedings of Allerton Conference on Communication, Control and Computing, Monticello, IL, Oct 2001 [SRK03] S Shakkotai, T Rappaport, and P Karlsson Cross-layer design for wireless networks IEEE Communications Magazine, 41(10):74–80, 2003 [SS02a] A Scaglione and S Servetto On the interdependence of routing and data compression in multihop-sensor networks In Proceedings of the 8th ACM International Conference on Mobile Computing and Networking (MobiCom 2002), pages 140–147, Atlanta, September 2002 [SS02b] S Shakkottai and A L Stolyar Scheduling for multiple flows sharing a time-varying channel: The exponential rule Analytic Methods in Applied Probability In Memory of Fridrih Karpelevich Yu M Suhov, Editor, 207:185–202, 2002 [SSZ01] I Stojmenovic, M Seddigh, and J Zunic Internal nodes based broadcasting in wireless networks In Proceeding of the 34th Hawaii International Conference on System Sciences, 2001 [SW73] D Slepian and K Wolf Noiseless encoding of correlated information sources IEEE Transactions on Information Theory, IT-19(4):471– 480, Jul 1973 [TE93] L Tassiulas and A Ephremides Dynamic server allocation to parallel queues with randomly varying connectivity IEEE Transactions Information Theory, 39(2):466–478, Mar 1993 [TH98a] D Tse and S Hanly Multi-access fading channels: Part i: Polymatroid structure, optimal resource allocation and through-put capacities IEEE Transactions on Information Theory, 44(7):2796–2815, 1998 [TH98b] D Tse and S Hanly Multi-access fading channels: Part ii: Delaylimited capacities IEEE Transactions on Information Theory, 44(7):2816–2831, 1998 201 [VAT03] P Venkitasubramaniam, S Adireddy, and L Tong Opportunistic aloha and cross-layer design for sensor networks In Proceedings of IEEE MILCOM, Boston, Oct 2003 [Vin05] S Vineet Cross-layer design for communication systems Master’s thesis, National University of Singapore, 2005 [Vuc91] B Vucetic An adaptive coding scheme for time-varying channels IEEE Transactions Communications, 39(5):653–663, May 1991 [WM95] H S Wang and N Moayeri Finite-state markov channel - a useful model for radio communication channels IEEE Transactions on Vehicular Technology, 44(1):163–171, February 1995 [WNE00] J E Wieselthier, G D Nguyen, and A Ephremides On the construction and energy-efficient broadcast and multicast trees in wireless networks In Proceedings of IEEE INFOCOM’00, Tel-Aviv, Mar 2000 [WS95] W T Webb and R Steele Variable rate qam for mobile radio IEEE Transactions on Communications, 43(7):2223–2230, July 1995 [WZ76] A D Wyner and J Ziv The rate-distortion function for source coding with side information at decoder IEEE Trans on Inform Theory, IT-22(1):1–10, January 1976 [ZW02] D Zhang and K Wasserman Transmission schemes for timevarying wireless channels with partial state observations In Proceedings of IEEE INFOCOM’02, pages 467–476, New York, Jun 2002